# Tag Info

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You could try one of these tools to predict protein-protein interactions: Struct2Net Given two protein sequences, the structure-based interaction prediction technique threads these two sequences to all the protein complexes in the PDB and then chooses the best potential match. Based on this match, the method generates alignment scores, z-scores, and an ...

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It is a crystallisation artefact. Namely, the protein are placed in a condition where they fall out of solution without aggregating a gloop (as happens in most well in xstal trial) and to pack as a crystal they need to be placed nice and orderly. It is coordinated by water so is not relevant. A lot of structures have these —DMSO and ions are the most ...

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Your list has two identifiers for the same node per line. In order to use it, you will need to change that. If you want to use the gene name (2nd and 4th fields, in your example), just run: awk 'print $2,$4' netw.txt > netw.gr If you want to use the Entrez geneIDs instead, run: awk 'print $1,$3' netw.txt > netw.gr Then, as others already mentioned, ...

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Proving a negative is hard, but to my knowledge I do not know a database that has all the structure of drug bound protein. I can give some close matches. First a few terms to be on the same page. A ligand is something that binds, which can be physiological, such as a cofactor or substrate, or not (especially man-made), such as a drug, i.e. a compound that ...

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since I am not yet allowed to comment, I will have to pots this as an answer. I guess Cytoscape (http://cytoscape.org/) would be a nice way to analyse such interaction lists/networks (still depends a bit on what you plan to do). However, for this you will have to preprocess the downloadable excel sheet a bit. save as tab-separated file (tsv) run the below ...

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You can start by looking at the correlation (if data is non-parametric) of each variable with each variable of each data point you have. One thing that might change is the correlation between variables along the disease. Next you can determine which relationships are constant along the disease progression/clinical trial. Identify also the ones that don't ...

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IBIS reports protein interaction with other biomolecules. Your protein or homolog must exist in the Protein Data Bank. IBIS @ NCBI: https://www.ncbi.nlm.nih.gov/Structure/ibis/ibis.cgi "For a given protein sequence or structure query, IBIS reports protein-protein, protein-small molecule, protein nucleic acids and protein-ion interactions observed in ...

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I'm not sure I understand "I would like to run GSEA or a similar analysis to find WGCNCA clusters that differ based on the interaction between the two main variables". I would run an association analysis (regression) for module eigengenes and the appropriate interaction term. The (most) significant modules are your candidates. This step is simply ...

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This is a comment to redirect to other answers, but go too long. Docking is the in silico prediction of where a ligand binds in a protein. There are several Q&As about docking here that are relevant, so please check out: machine learning, docking and the factors that affect it: Generate ligands candidates based on protein shape —also worth a read the &...

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If you are training a machine learning algorithm you probably want to train against things you know to be true, rather than making predictions from the sequence and training against the predictions. In this case you will want to use protein structures with the relevant ligands bound as your data. If you go to the RCSB PDB advanced search you can search for ...

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If you were to treat the nucleotide like a ligand you could follow these steps on pymol, and find the measurements you desire. Calculating distances to centroids is a little bit more complex, as you have to make a pseudo atom to measure against.

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If you have information on which part of the protein are interacting, and its sequence, you can search for homologous or similar sequences in other proteins. This would give you a list of potential targets and probably a similarity score which you can also use. Then you would have something to run simulations on. And I think that with 3D structure you ...

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This is something that Qiagen's Ingenuity Pathway Analysis is meant to solve. Unfortunately, the process of solving pathway dependency problems like these is difficult, and frequently leads to the discovery of super-linked proteins that are seen everywhere (and consequently fairly uninteresting from a biological perspective). The challenge is in filtering ...

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Several have mentioned the excellent Cytoscape desktop package for visualising interactions as a graph. If you want to build your own web-based visualisations (rather than using a pre-built website to visualise them) then the Cytoscape javascript library might fulfil your needs.

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Usually interactions are represented in a graph, with edges representing the interactions (colors, thickness or values on the edges represent different properties of those edges). As far as I know, there aren't on line programs to represent this kind of information. For an off line representation, you can use the igraph package in R, or Cytoscape program.

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